Primary mitochondrial respiratory chain (MRC) disease causes an extensive array of multi-system findings characterized by impaired energy metabolism that affects 1 in 5,000 individuals across a lifetime. Unfortunately, diagnosis of this class of disorders is complicated by the absence of a biomarker that divulges all cases with sufficient sensitivity or specificity. Thus, despite arduous diagnostic efforts, objective evidence of mitochondrial dysfunction is commonly not obtained for clinically suspected mitochondrial disease patients. Significant bioinformatic advances have made it feasible to consider addressing this diagnostic challenge from a novel, systems-biology perspective. The proposed approach is based on the hypothesis that MRC dysfunction in humans is accompanied by cellular adaptations identifiable at the level of biochemical pathway expression alterations. The specific aim of this proposal is to determine if transcriptional alterations across biochemical pathways occur in patients with confirmed MRC disease. Recognition of specific adaptive changes in gene expression patterns among biologically-relevant pathways in human tissues will permit the pursuit of two overall goals. First, is to elucidate biochemical mechanisms by which primary MRC dysfunction results in clinical disease. This will provide insight into secondary metabolic consequences of genetically-based mitochondrial disease which may be amenable to therapeutic intervention. Second, is to identify a "signature" of primary MRC dysfunction in humans based on these biochemical pathway expression alterations. This will permit the development of a systems biology-based "biomarker" with the potential to guide the molecular diagnosis of human MRC disease. The long-term objective is to develop a minimally-invasive screening assay used to estimate the likelihood of primary MRC disease in suspected patients regardless of individual pathogenic cause. This grant proposes to apply biochemical pathway cluster analysis to global genome transcriptional profiling in tissues from clinically symptomatic patients with confirmed MRC disease. Global genome expression patterns will be studied by Affymetrix microarray analysis using RNA isolated both from skeletal muscle tissue (subaim A) and from minimally-invasively obtained specimens (cultured fibroblast cell lines and blood lymphoblastoid cell lines) (subaim B) in patients with biopsy-proven MRC dysfunction and, when possible, confirmed pathogenic mutations. Multiple tissues will be studied from a given MRC disease patient in an effort to determine if identified expression alterations are unique to clinically affected tissue or common to minimally-invasively obtained, asymptomatic tissues as well. Age-, gender-, and race-matched control specimens will be carefully selected for each tissue type. Data analysis will primarily utilize gene set enrichment analysis of biochemical pathway clusters curated from in silico databases.